import os

id_str = '1804031938'

batch_size = 32

# predict_batch_size = 256

train_batch_nums = [10000]

lrs = [0.03]

eval_internal = 100
eval_print_batch_interval = 20
print_batch_interval = 20

persistent_internal = 2000

train_k_prob = 0.5

os.environ["CUDA_VISIBLE_DEVICES"] = '2, 3'

feature_size = 400

cnn1_kernel = [1, 12, 1, 6]
cnn2_kernel = [1, 8, 6, 10]

rnn_hidden_size = 128

fc1_b = 64

emos = ['neu', 'ang', 'hap', 'sad']

optimizer_type = 'adam'

persist_checkpoint_file = './p-model/my-model-' + id_str

gt_npy = './result/gt_' + id_str + '.npy'
pr_npy = './result/pr_' + id_str + '.npy'

is_train = True

is_restore = False

restore_file = './p-model/my-model-2'
restart_i = 0

out_put_log = id_str+'.log'

# remote data_dir limit len
# _data_dir = '/home/ddy/projects/emotions/iemocap_4emo_spectr_norm_limitlen'

# remote data_dir
# _data_dir = '/home/ddy/projects/emotions/iemocap_4emo_spectr_norm'

# # # remote simple data_dir
# _data_dir = '/home/ddy/projects/emotions/iemocap_4emo_spectr_norm_simple'

# # local(mac) simple data_dir
# _data_dir = '/Users/d/Project/emotions/data/iemocap_4emo_spectr_norm_simple_limitlen'

_data_dir = '/home/ddy/projects/emotions/Spectrogram_EN_Var_tfrecord_limitlen'

_train_filename = 'train.tfrecords'
_vali_filename = 'vali.tfrecords'
_test_filename = 'test.tfrecords'

train_filepath = os.path.join(_data_dir, _train_filename)
vali_filepath = os.path.join(_data_dir, _vali_filename)
test_filepath = os.path.join(_data_dir, _test_filename)

# data size: Session1 942, Session2 813, Session3 1000,(2755) Session4 793, Session5 942
